You can find all related materials, links and references there.
December 16, 2019
This presentation
and the entire research archive is available on:
Content of the Presentation
- Introduction
- Methodology
- Simulation Results
- Discussion
Introduction
Multiple imputation
- Missing data is ubiquitous (Allison 2001).
- Ad hoc solutions may yield invalid inferences (Van Buuren 2018).
- Rubin (1987) proposed the framework of MI.
Multiple imputation
Algorithmic convergence
Methodology
Mock code
simulation <- function() {
mvtnorm(X,Z1,Z2) %>%
mutate(Y~X+Z1+Z2) %>%
for (max_iterations in 1:100) {
ampute() %>%
impute() %T%
convergence_diagnostics %>%
lm(Y~X+Z1+Z2) %>%
pool %>%
simulation_diagnostics %>%
c(., convergence_diagnostics)
}
}
replicate(simulation, n = 1000)
Simulation Results
Results
Results
simulation diagnostics versus convergence diagnostics
Inspect the autocorrelation dip
Discussion
Discussion
Impression of ShinyMICE
Inspect \(\widehat{R}\)
Inspect autocorrelation
Equations \(\widehat{R}\)
[]
References
Allison, Paul D. 2001. Missing Data. Sage publications.
Rubin, Donald B. 1987. Multiple Imputation for Nonresponse in Surveys. Wiley Series in Probability and Mathematical Statistics Applied Probability and Statistics. New York, NY: Wiley.
Van Buuren, Stef. 2018. Flexible Imputation of Missing Data. Chapman; Hall/CRC.